cats-scoring 1.3.0

Contextual Ambiguity & Trust Scoring — trust intelligence for OSINT sources

CATS — Contextual Ambiguity & Trust Scoring

Trust intelligence for OSINT sources — not fact-checking, but source reliability over time.

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What is CATS?

❌ Fact-checking	✅ CATS
"Is this information true?"	"How reliable is this source, in this context, right now?"

CATS analyses the behavioural patterns of a source over time — narrative consistency, sentiment volatility, temporal gaps, and signs of algorithmic manipulation — and returns a transparent, explainable trust score.

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Signals

Signal	What it measures	Method
Coherence	Entity/argument consistency across messages	spaCy NER + Jaccard (or optional Sentence-BERT) similarity
Volatility	Abrupt narrative tone changes	TextBlob (or optional BERT) sentiment spike detection
Silence	Anomalous temporal gaps in publishing	Gap analysis vs. source-type thresholds
Gaming	Signs of algorithmic manipulation	Repetition + TTR + burst + vocab diversity

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Try it in 5 lines (no infrastructure)

No database, no Redis, no API keys — the signal pipeline as a plain library call:

from cats.lite import score

result = score([
    {"timestamp": "2026-01-01T08:00:00Z", "text": "Il governo annuncia un piano economico."},
    {"timestamp": "2026-01-01T12:00:00Z", "text": "I sindacati commentano il piano."},
    {"timestamp": "2026-01-02T09:00:00Z", "text": "Il parlamento discute la legge di bilancio."},
], source_type="news")

print(result["trust_score"], result["band"], result["explanation"]["primary_driver"])

Install with pip install -r requirements.txt (plus make nlp-download for full-fidelity coherence — without it the NER backend degrades to a neutral value). The full API below adds persistence, auditing and GDPR endpoints.

Or try it in the browser:

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Quick Start (full deployment)

# 1. Clone and configure
git clone https://github.com/Leapfrog-LSA/CATS-Contextual-Ambiguity-Trust-Scoring.git && cd CATS-Contextual-Ambiguity-Trust-Scoring
cp .env.example .env          # fill in secrets (see .env.example)

# 2. Install
make dev-install              # deps + pre-commit hooks
make nlp-download             # spaCy it_core_news_lg + TextBlob corpora

# 3. Start services and run
make docker-up                # PostgreSQL 16 + Redis 7
make db-migrate               # Alembic migrations
uvicorn cats.api.main:app --reload

# 4. Test
make test

Generate a secure AUDIT_ENCRYPTION_KEY: make generate-key

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API Example

curl -s -X POST http://localhost:8000/v1/cats/evaluate \
  -H "Authorization: Bearer $CATS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source_id": "twitter:example_handle",
    "messages": [
      {"timestamp": "2026-01-01T08:00:00Z", "text": "Governo annuncia piano economico."},
      {"timestamp": "2026-01-01T09:00:00Z", "text": "Protesta dei lavoratori in piazza."},
      {"timestamp": "2026-01-01T10:00:00Z", "text": "Parlamento discute la legge di bilancio."}
    ],
    "context": {"source_type": "social"}
  }' | jq

{
  "trace_id": "550e8400-e29b-41d4-a716-446655440000",
  "score": 68.4,
  "band": "medium_high",
  "requires_review": false,
  "signals": [
    {"name": "coherence",  "value": 71.2, "confidence": 0.3},
    {"name": "volatility", "value": 55.0, "confidence": 0.15},
    {"name": "silence",    "value": 0.0,  "confidence": 0.1},
    {"name": "gaming",     "value": 12.8, "confidence": 0.06}
  ]
}

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Trust Score Bands

Score	Band	Recommended Action
80–100	high	Usable for OSINT
60–79	medium_high	Cross-validate key claims
40–59	medium	Human review recommended
20–39	low	Human review required
0–19	very_low	Do not use without validation

⚠️ Scores are ordinal rankings, not absolute probabilities (WP 4.3).

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Architecture

Client (HTTPS + Bearer token)
        │
   nginx (TLS 1.3 · rate 30 req/min)
        │
   FastAPI — 9-phase pipeline
   ├─ POST /v1/cats/evaluate
   ├─ POST /v1/cats/batch                ← evaluate up to 50 sources at once
   ├─ GET  /v1/cats/explain/{trace_id}   ← GDPR Art.14/22
   ├─ POST /v1/cats/contest/{trace_id}   ← GDPR Art.22
   ├─ GET  /v1/cats/stats
   └─ GET  /health  /metrics
        │                │
     Redis 7          PostgreSQL 16
  (rate limiting)   (AES-256 audit log)
                    + APScheduler purge

The nginx reverse proxy (TLS, rate limiting, security headers) is configured in deploy/nginx.conf and started by make docker-up.

See docs/architecture.md for full signal and security details.

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Documentation

Document	Description
docs/api.md	Full API reference
docs/architecture.md	Signal algorithms, weight matrix, security design
docs/compliance.md	GDPR + EU AI Act compliance
docs/eu_ai_act/	EU AI Act conformity scaffold (Annex IV, Art. 9/10)
docs/calibration.md	Empirical weight calibration (genetic search)
CHANGELOG.md	Version history
CONTRIBUTING.md	Development guide
SECURITY.md	Vulnerability reporting

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Known Limitations (WP 4.1)

• NLP accuracy ~55–62% (default): spaCy NER + TextBlob; optional BERT sentiment and Sentence-BERT coherence backends are available for higher accuracy (see .env.example)
• Uncalibrated parameters: thresholds are initial estimates; signal weights can now be empirically tuned with cats.calibration, but band thresholds remain unvalidated
• Small validation set (July 2026): calibration/validation currently rests on 50 RSS-labelled sources; see calibration findings for the honest numbers (full-dataset concordance 0.78, holdout 0.71) and their caveats
• Italian-optimised: using it_core_news_lg; other languages degrade accuracy
• Ordinal scoring only: not suitable as sole basis for autonomous decisions

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Roadmap

Version	Status	Key features
v1.0	✅	spaCy NER · 9-phase pipeline · GDPR API · Docker
v1.1	✅	BERT Italian sentiment · multi-tenant PostgreSQL · batch endpoint · Prometheus /metrics · nginx
v1.2	✅	Sentence-BERT coherence · explainer attribution · weight calibration
v1.3	✅	Signal-polarity fix in aggregation · distant-supervision dataset (MBFC + disinfo networks) · snapshot accumulation
v2.0	2027	AUC-ROC ≥ 0.78 · full EU AI Act Annex IV technical documentation

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License

MIT — [e-mail]